mlsanityData to 7 Oct 2026

MathPrompter: Mathematical Reasoning using Large Language Models

arXiv 2303.05398 ↗First posted 4 Mar 2023cs.CL cs.AIcited by 236

On the map: LLM reasoning → Mathematical reasoning

Abstract (arXiv)

Large Language Models (LLMs) have limited performance when solving arithmetic reasoning tasks and often provide incorrect answers. Unlike natural language understanding, math problems typically have a single correct answer, making the task of generating accurate solutions more challenging for LLMs. To the best of our knowledge, we are not aware of any LLMs that indicate their level of confidence in their responses which fuels a trust deficit in these models impeding their adoption. To address this deficiency, we propose `MathPrompter', a technique that improves performance of LLMs on arithmetic problems along with increased reliance in the predictions. MathPrompter uses the Zero-shot chain-of-thought prompting technique to generate multiple Algebraic expressions or Python functions to solve the same math problem in different ways and thereby raise the confidence level in the output results. This is in contrast to other prompt based CoT methods, where there is no check on the validity of the intermediate steps followed. Our technique improves over state-of-the-art on the MultiArith dataset (78.7%rightarrow92.5%) evaluated using 175B parameter GPT-based LLM.

Key facts. Cited by 236 papers in the map (data through 7 Oct 2026). Placed in Mathematical reasoning, one of 17 directions in LLM reasoning. Builds on 21 of its 26 references that are in the map. First posted on arXiv 4 Mar 2023.

Builds on

21 of 26 references.

References · 21

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  5. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  6. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  7. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  8. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  9. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  10. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  11. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
  12. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,793
9 more
  1. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,255
  2. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  3. 2021Scaling Language Models: Methods, Analysis & Insights from Training Gophercited by 1,199
  4. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
  5. 2021Are NLP Models really able to Solve Simple Math Word Problems?cited by 861
  6. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
  7. 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 668
  8. 2021Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigmcited by 599
  9. 2016Solving General Arithmetic Word Problemscited by 353

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 61; 2024: 91; 2025: 69; 2026: 15.

236 papers.

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  1. 2023A Survey of Large Language Modelscited by 2,537
  2. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,348
  3. 2023MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Modelscited by 724
  4. 2023A Comprehensive Overview of Large Language Modelscited by 639
  5. 2023WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instructcited by 618
  6. 2023Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoningcited by 379
  7. 2023Chameleon: Plug-and-Play Compositional Reasoning with Large Language Modelscited by 352
  8. 2023JudgeLM: Fine-tuned Large Language Models are Scalable Judgescited by 351
  9. 2024Discrete Flow Matchingcited by 301
  10. 2023ToolQA: A Dataset for LLM Question Answering with External Toolscited by 273
  11. 2025ZeroSearch: Incentivize the Search Capability of LLMs without Searchingcited by 203
  12. 2024Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understandingcited by 185
38 more
  1. 2024Agent AI: Surveying the Horizons of Multimodal Interactioncited by 167
  2. 2024GraphRouter: A Graph-based Router for LLM Selectionscited by 154
  3. 2023Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoningcited by 140
  4. 2024Multi-Step Reasoning with Large Language Models, a Surveycited by 140
  5. 2023Generalized Planning in PDDL Domains with Pretrained Large Language Modelscited by 139
  6. 2024OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learningcited by 124
  7. 2024Taming Overconfidence in LLMs: Reward Calibration in RLHFcited by 105
  8. 2025Agentic Large Language Models, a surveycited by 105
  9. 2023ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Searchcited by 103
  10. 2023Amortizing intractable inference in large language modelscited by 94
  11. 20233D-GPT: Procedural 3D Modeling with Large Language Modelscited by 91
  12. 2023MindAgent: Emergent Gaming Interactioncited by 87
  13. 2024Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learningcited by 79
  14. 2023Temporal Data Meets LLM -- Explainable Financial Time Series Forecastingcited by 77
  15. 2023Applying Large Language Models and Chain-of-Thought for Automatic Scoringcited by 68
  16. 2023Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Modelscited by 66
  17. 2023Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyondcited by 65
  18. 2024Fight Back Against Jailbreaking via Prompt Adversarial Tuningcited by 63
  19. 2023Towards Better Chain-of-Thought Prompting Strategies: A Surveycited by 62
  20. 2023TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPTcited by 57
  21. 2024Unlocking the Capabilities of Thought: A Reasoning Boundary Framework to Quantify and Optimize Chain-of-Thoughtcited by 56
  22. 2024Don't Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalizationcited by 53
  23. 2024What's Wrong with Your Code Generated by Large Language Models? An Extensive Studycited by 52
  24. 2025A Survey on Large Language Models for Mathematical Reasoningcited by 52
  25. 2024Efficient Causal Graph Discovery Using Large Language Modelscited by 51
  26. 2023Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoningcited by 49
  27. 2023Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4cited by 48
  28. 2023LayoutPrompter: Awaken the Design Ability of Large Language Modelscited by 47
  29. 2025VLM-R3: Region Recognition, Reasoning, and Refinement for Enhanced Multimodal Chain-of-Thoughtcited by 47
  30. 2024A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Taskscited by 46
  31. 2024Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolationcited by 45
  32. 2024Navigating the Safety Landscape: Measuring Risks in Finetuning Large Language Modelscited by 45
  33. 2024Interpreting and Improving Large Language Models in Arithmetic Calculationcited by 43
  34. 2023A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verificationcited by 42
  35. 2025Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lenscited by 42
  36. 2025Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement Learningcited by 37
  37. 2024Capabilities of Large Language Models in Control Engineering: A Benchmark Study on GPT-4, Claude 3 Opus, and Gemini 1.0 Ultracited by 36
  38. 2025Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?cited by 36

50 most cited of 236 All 236 (JSON)

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  1. 2025Do LLMs Overthink Basic Math Reasoning? Benchmarking the Accuracy-Efficiency Tradeoff in Language Models
  2. 2024Benchmarking Large Language Models for Math Reasoning Tasks
  3. 2024MARIO: MAth Reasoning with code Interpreter Output -- A Reproducible Pipeline
  4. 2024Arithmetic Reasoning with LLM: Prolog Generation & Permutation
  5. 2024MindStar: Enhancing Math Reasoning in Pre-trained LLMs at Inference Time
  6. 2024Evaluating Mathematical Reasoning of Large Language Models: A Focus on Error Identification and Correction
  7. 2025Mathematical Reasoning in Large Language Models: Assessing Logical and Arithmetic Errors across Wide Numerical Ranges
  8. 2024Investigating Symbolic Capabilities of Large Language Models